AI Engineer

Capgemini; Charlotte (NC); Presencial; USD 180.000 - 230.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical

Dental

Vision

Retirement benefits

Descripción de la vacante

Capgemini seeks an experienced platform engineer to build and scale AI platform infrastructure in Charlotte, NC. You will design scalable GCP cloud infrastructure for AI/ML workloads and manage CI/CD pipelines and MLOps pipelines across the ML lifecycle.

You will containerize services with Docker/Kubernetes, implement IaC with Terraform, and ensure security and observability. This is an onsite contract role with Capgemini, partnering with AI/ML teams to productionize models.

Formación

• 7+ years of experience in platform engineering, DevOps, or infrastructure engineering.
• Hands-on expertise in Google Cloud Platform (GCP) including Vertex AI, GKE, Cloud Run, IAM, VPC/networking, and BigQuery.
• Deep experience designing and managing CI/CD pipelines (Cloud Build, Jenkins, GitHub Actions, GitLab CI, ArgoCD).
• Proficiency with Infrastructure as Code (Terraform preferred).
• Strong scripting/programming in Python, Bash, or Go.
• Experience with containers and orchestration (Docker, Kubernetes).
• Familiarity with MLOps across ML lifecycle: training, versioning, deployment, monitoring.
• Observability tooling such as Cloud Monitoring, Prometheus, Grafana, ELK/EFK.
• Cloud security, IAM, and secrets management best practices.

Responsabilidades

• Design, build, and maintain scalable GCP cloud infrastructure for AI/ML and application workloads.
• Architect and manage CI/CD pipelines for model training, deployment, and application release workflows.
• Create and run MLOps pipelines for model versioning, training, deployment, and monitoring.
• Use Infrastructure as Code for repeatable, auditable environment provisioning.
• Containerize and orchestrate services with Docker and Kubernetes (GKE).
• Set up observability including logging and alerting for AI/ML services.
• Partner with AI/ML engineers and data scientists to productionize models.
• Implement platform security with IAM, secrets management, and compliance.
• Drive automation to reduce manual toil across build, test, deployment, rollback workflows.
• Troubleshoot platform and infrastructure issues and lead root-cause resolution.

Conocimientos

Cloud platform engineering

DevOps

CI/CD pipelines

MLOps

Scripting (Python/Bash/Go)

Security and IAM

Herramientas

GCP

Vertex AI

GKE

Cloud Run

Terraform

Deployment Manager

Docker

Kubernetes

GitHub Actions

Jenkins

GitLab CI

ArgoCD

Kubeflow

MLflow

Prometheus

Grafana

Cloud Monitoring/Cloud Logging

ELK/EFK

Descripción del empleo

Build and scale AI platform infrastructure for AI/ML and generative AI workloads on Google Cloud at Capgemini. This contract role is based in Charlotte, NC (onsite) and focuses on cloud platform engineering, CI/CD automation, and MLOps practices to support reliable, secure, and scalable delivery. If you enjoy turning experimentation into production-ready systems, you will work closely with AI/ML engineers and data scientists to streamline the full path from training to monitoring.

What you’ll do

• Design, build, and maintain scalable GCP cloud infrastructure for AI/ML and application workloads
• Architect and manage CI/CD pipelines for model training, deployment, and application release workflows (Cloud Build, GitHub Actions, Jenkins, GitLab CI, or similar)
• Create and run MLOps pipelines for model versioning, training, deployment, and monitoring using tools such as Vertex AI Pipelines, Kubeflow, and MLflow
• Use Infrastructure as Code (Terraform, Deployment Manager) for repeatable and auditable environment provisioning
• Containerize and orchestrate services with Docker and Kubernetes (GKE)
• Set up observability including logging and alerting for AI/ML services using Cloud Monitoring, Cloud Logging, and Prometheus/Grafana
• Partner with AI/ML engineers and data scientists to productionize models and reduce friction between experimentation and deployment
• Implement platform security with IAM, secrets management, and compliance across environments
• Drive automation to reduce manual toil across build, test, deployment, and rollback workflows
• Troubleshoot platform and infrastructure issues and lead root-cause resolution for production incidents

What you bring

• 7+ years of experience in platform engineering, DevOps, or infrastructure engineering
• Hands-on expertise in Google Cloud Platform (GCP), including Vertex AI, GKE, Cloud Run, IAM, VPC/networking, and BigQuery
• Deep experience designing and managing CI/CD pipelines, including options such as Cloud Build, Jenkins, GitHub Actions, GitLab CI, and ArgoCD
• Proficiency with Infrastructure as Code (Terraform preferred)
• Strong scripting and programming skills in Python, Bash, or Go
• Experience with containerization and orchestration (Docker, Kubernetes)
• Working knowledge of MLOps concepts across the ML lifecycle: training, versioning, deployment, and monitoring
• Experience with observability tooling such as Cloud Monitoring, Prometheus, Grafana, and ELK/EFK
• Strong understanding of cloud security, IAM, and secrets management best practices

Tech stack you may work with

GCP, Vertex AI, GKE, Cloud Run, IAM, VPC/networking, BigQuery, Cloud Build, GitHub Actions, Jenkins, GitLab CI, Vertex AI Pipelines, Kubeflow, MLflow, Terraform, Deployment Manager, Docker, Kubernetes, Prometheus, Grafana, Cloud Monitoring, Cloud Logging, Python, Bash, Go, ELK/EFK, ArgoCD.

Benefits

• Medical, dental, vision, and retirement benefits